Detecting frames in news headlines and its application to analyzing news framing trends surrounding U.S. gun violence

Different news articles about the same topic often offer a variety of perspectives: an article written about gun violence might emphasize gun control, while another might promote 2nd Amendment rights, and yet a third might focus on mental health issues. In communication research, these different perspectives are known as “frames”, which, when used in news media will influence the opinion of their readers in multiple ways. In this paper, we present a method for effectively detecting frames in news headlines. Our training and performance evaluation is based on a new dataset of news headlines related to the issue of gun violence in the United States. This Gun Violence Frame Corpus (GVFC) was curated and annotated by journalism and communication experts. Our proposed approach sets a new state-of-the-art performance for multiclass news frame detection, significantly outperforming a recent baseline by 35.9% absolute difference in accuracy. We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U.S. between 2016 and 2018.Published versio

Issue Framing in Online Discussion Fora

In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic. In social science, this is referred to as issue framing. In this paper, we introduce a new issue frame annotated corpus of online discussions. We explore to what extent models trained to detect issue frames in newswire and social media can be transferred to the domain of discussion fora, using a combination of multi-task and adversarial training, assuming only unlabeled training data in the target domain.Comment: To appear in NAACL-HLT 201

Aspect-Controlled Neural Argument Generation

We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, stance, and aspect. We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects. Our evaluation shows that our generation model is able to generate high-quality, aspect-specific arguments. Moreover, these arguments can be used to improve the performance of stance detection models via data augmentation and to generate counter-arguments. We publish all datasets and code to fine-tune the language model

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Fine-grained Subjectivity and Sentiment Analysis: Recognizing the intensity, polarity, and attitudes of private states

Private states (mental and emotional states) are part of the information that is conveyed in many forms of discourse. News articles often report emotional responses to news stories; editorials, reviews, and weblogs convey opinions and beliefs. This dissertation investigates the manual and automatic identification of linguistic expressions of private states in a corpus of news documents from the world press. A term for the linguistic expression of private states is subjectivity.The conceptual representation of private states used in this dissertation is that of Wiebe et al. (2005). As part of this research, annotators are trained to identify expressions of private states and their properties, such as the source and the intensity of the private state. This dissertation then extends the conceptual representation of private states to better model the attitudes and targets of private states. The inter-annotator agreement studies conducted for this dissertation show that the various concepts in the original and extended representation of private states can be reliably annotated.Exploring the automatic recognition of various types of private states is also a large part of this dissertation. Experiments are conducted that focus on three types of fine-grained subjectivity analysis: recognizing the intensity of clauses and sentences, recognizing the contextual polarity of words and phrases, and recognizing the attribution levels where sentiment and arguing attitudes are expressed. Various supervised machine learning algorithms are used to train automatic systems to perform each of these tasks. These experiments result in automatic systems for performing fine-grained subjectivity analysis that significantly outperform baseline systems